Systematic and Comprehensive Review of Clustering and Multi-Target Tracking Techniques for LiDAR Point Clouds in Autonomous Driving Applications
Autonomous vehicles (AVs) rely on advanced sensory systems, such as Light Detection and Ranging (LiDAR), to function seamlessly in intricate and dynamic environments. LiDAR produces highly accurate 3D point clouds, which are vital for the detection, classification, and tracking of multiple targets....
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/13/6119 |
| _version_ | 1797590814157701120 |
|---|---|
| author | Muhammad Adnan Giulia Slavic David Martin Gomez Lucio Marcenaro Carlo Regazzoni |
| author_facet | Muhammad Adnan Giulia Slavic David Martin Gomez Lucio Marcenaro Carlo Regazzoni |
| author_sort | Muhammad Adnan |
| collection | DOAJ |
| description | Autonomous vehicles (AVs) rely on advanced sensory systems, such as Light Detection and Ranging (LiDAR), to function seamlessly in intricate and dynamic environments. LiDAR produces highly accurate 3D point clouds, which are vital for the detection, classification, and tracking of multiple targets. A systematic review and classification of various clustering and Multi-Target Tracking (MTT) techniques are necessary due to the inherent challenges posed by LiDAR data, such as density, noise, and varying sampling rates. As part of this study, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was employed to examine the challenges and advancements in MTT techniques and clustering for LiDAR point clouds within the context of autonomous driving. Searches were conducted in major databases such as IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, and Google Scholar, utilizing customized search strategies. We identified and critically reviewed 76 relevant studies based on rigorous screening and evaluation processes, assessing their methodological quality, data handling adequacy, and reporting compliance. As a result of this comprehensive review and classification, we were able to provide a detailed overview of current challenges, research gaps, and advancements in clustering and MTT techniques for LiDAR point clouds, thus contributing to the field of autonomous driving. Researchers and practitioners working in the field of autonomous driving will benefit from this study, which was characterized by transparency and reproducibility on a systematic basis. |
| first_indexed | 2024-03-11T01:29:44Z |
| format | Article |
| id | doaj.art-d24afc824a284a7cbbfbe8910680b356 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:29:44Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d24afc824a284a7cbbfbe8910680b3562023-11-18T17:30:48ZengMDPI AGSensors1424-82202023-07-012313611910.3390/s23136119Systematic and Comprehensive Review of Clustering and Multi-Target Tracking Techniques for LiDAR Point Clouds in Autonomous Driving ApplicationsMuhammad Adnan0Giulia Slavic1David Martin Gomez2Lucio Marcenaro3Carlo Regazzoni4Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture (DITEN), University of Genova, Via Opera Pia 11a, I-16145 Genoa, ItalyDepartment of Electrical, Electronic, Telecommunications Engineering and Naval Architecture (DITEN), University of Genova, Via Opera Pia 11a, I-16145 Genoa, ItalyDepartamento de Ingeniería de Sistemas y Automática, Universidad Carlos III de Madrid, Butarque 15, Leganés, 28911 Madrid, SpainDepartment of Electrical, Electronic, Telecommunications Engineering and Naval Architecture (DITEN), University of Genova, Via Opera Pia 11a, I-16145 Genoa, ItalyDepartment of Electrical, Electronic, Telecommunications Engineering and Naval Architecture (DITEN), University of Genova, Via Opera Pia 11a, I-16145 Genoa, ItalyAutonomous vehicles (AVs) rely on advanced sensory systems, such as Light Detection and Ranging (LiDAR), to function seamlessly in intricate and dynamic environments. LiDAR produces highly accurate 3D point clouds, which are vital for the detection, classification, and tracking of multiple targets. A systematic review and classification of various clustering and Multi-Target Tracking (MTT) techniques are necessary due to the inherent challenges posed by LiDAR data, such as density, noise, and varying sampling rates. As part of this study, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was employed to examine the challenges and advancements in MTT techniques and clustering for LiDAR point clouds within the context of autonomous driving. Searches were conducted in major databases such as IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, and Google Scholar, utilizing customized search strategies. We identified and critically reviewed 76 relevant studies based on rigorous screening and evaluation processes, assessing their methodological quality, data handling adequacy, and reporting compliance. As a result of this comprehensive review and classification, we were able to provide a detailed overview of current challenges, research gaps, and advancements in clustering and MTT techniques for LiDAR point clouds, thus contributing to the field of autonomous driving. Researchers and practitioners working in the field of autonomous driving will benefit from this study, which was characterized by transparency and reproducibility on a systematic basis.https://www.mdpi.com/1424-8220/23/13/6119autonomous vehicles (AVs)LiDAR (Light Detection and Ranging)point cloudsclustering algorithmsMulti-Target Tracking (MTT)object detection |
| spellingShingle | Muhammad Adnan Giulia Slavic David Martin Gomez Lucio Marcenaro Carlo Regazzoni Systematic and Comprehensive Review of Clustering and Multi-Target Tracking Techniques for LiDAR Point Clouds in Autonomous Driving Applications Sensors autonomous vehicles (AVs) LiDAR (Light Detection and Ranging) point clouds clustering algorithms Multi-Target Tracking (MTT) object detection |
| title | Systematic and Comprehensive Review of Clustering and Multi-Target Tracking Techniques for LiDAR Point Clouds in Autonomous Driving Applications |
| title_full | Systematic and Comprehensive Review of Clustering and Multi-Target Tracking Techniques for LiDAR Point Clouds in Autonomous Driving Applications |
| title_fullStr | Systematic and Comprehensive Review of Clustering and Multi-Target Tracking Techniques for LiDAR Point Clouds in Autonomous Driving Applications |
| title_full_unstemmed | Systematic and Comprehensive Review of Clustering and Multi-Target Tracking Techniques for LiDAR Point Clouds in Autonomous Driving Applications |
| title_short | Systematic and Comprehensive Review of Clustering and Multi-Target Tracking Techniques for LiDAR Point Clouds in Autonomous Driving Applications |
| title_sort | systematic and comprehensive review of clustering and multi target tracking techniques for lidar point clouds in autonomous driving applications |
| topic | autonomous vehicles (AVs) LiDAR (Light Detection and Ranging) point clouds clustering algorithms Multi-Target Tracking (MTT) object detection |
| url | https://www.mdpi.com/1424-8220/23/13/6119 |
| work_keys_str_mv | AT muhammadadnan systematicandcomprehensivereviewofclusteringandmultitargettrackingtechniquesforlidarpointcloudsinautonomousdrivingapplications AT giuliaslavic systematicandcomprehensivereviewofclusteringandmultitargettrackingtechniquesforlidarpointcloudsinautonomousdrivingapplications AT davidmartingomez systematicandcomprehensivereviewofclusteringandmultitargettrackingtechniquesforlidarpointcloudsinautonomousdrivingapplications AT luciomarcenaro systematicandcomprehensivereviewofclusteringandmultitargettrackingtechniquesforlidarpointcloudsinautonomousdrivingapplications AT carloregazzoni systematicandcomprehensivereviewofclusteringandmultitargettrackingtechniquesforlidarpointcloudsinautonomousdrivingapplications |